Search, Information, and Prices

Abstract

Consider a market with identical firms offering a homogeneous good. A consumer obtains price quotes from a subset of firms and buys from the firm offering the lowest price. The "price count" is the number of firms from which the consumer obtains a quote. For any given ex ante distribution of the price count, we obtain a tight upper bound (under first-order stochastic dominance) on the equilibrium distribution of sale prices. The bound holds across all models of firms' common-prior higher-order beliefs about the price count, including the extreme cases of full information (firms know the price count) and no information (firms only know the ex ante distribution of the price count). A qualitative implication of our results is that a small ex ante probability that the price count is one can lead to a large increase in the expected price. The bound also applies in a wide class of models where the price count distribution is endogenized, including models of simultaneous and sequential consumer search.

Patterns of Competitive Interaction

“…In this framework, there exists a symmetric equilibrium, and Johnen and Ronayne (2021) show that it is the unique equilibrium if and only if there are some consumers who consider precisely two firms. Bergemann, Brooks, and Morris (2021) extend Burdett and Judd's model so that firms have information about the number of firms a consumer considers. This information might be public, so all firms see the same information about a consumer, or different firms might have different information, and the authors derive the information structure that maximizes industry profit.…”

Strategic Limitation of Market Accessibility: Search Platform Design and Welfare

“…The search externality, however, is endogenous in a directed-search framework, and we show that it can also be affected by the market accessibility level. 6 See also Armstrong and Vickers (2019), Bergemann et al (2021), and Shi and Zhang (2023) who study market segmentation through information provision in models where sellers sell homogeneous goods and can price discriminate between captive and contested buyers. 7 This modelling approach was also adopted for example by Karle et al (2020).…”

Data, Competition, and Digital Platforms

“…At a broad level, this paper relates to information structures in advertising auctions, e.g., Bergemann et al (2021), and to nonlinear pricing, market segmentation, and competition, e.g., Bergemann et al (2015), Bonatti (2011), Elliott et al (2020), and Yang (2022. Finally, our analysis can be easily extended to discuss self-preferencing by a monopoly platform.…”